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<h1>Code from top papers</h1>

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<pre>
<h2>Code: in this repo</h2>
<a href="a01-PMID-39024031-Code.R">a01-PMID-39024031-Code.R</a>
	2024 BCMA CAR-T治疗的单细胞数据
	优点：细胞分群语句，三个层次细胞命名; sctransform + RPCA 整合多样本; 颜色设置包 paletteer;
	缺点：缺少更多细节

<a href="a02-argparse/">a02-argparse/</a> (<a href="a02-argparse/run_multiple_seurat.R">run_multiple_seurat.R</a> | <a href="a02-argparse/utils.R">utils.R</a>) 来源：群分享
	空间转录组 单脚本分析
	优点：
		R语言的 argparse 传递参数 --input
		source() 的使用
	缺点：很少注释；没有找到paper


<a href="a03-Treg-NC_2021-PMID34599187.Rmd">a03-Treg-NC_2021-PMID34599187.Rmd</a>
	2021 NC: CD177 modulates the function and homeostasis of tumor-infiltrating regulatory T cells
	we identify two distinct transcriptional fates for TI Treg cells, Fate-1 and Fate-2. The Fate-1 signature is associated with a poorer prognosis in ccRCC and several other solid cancers.
	优点: Rmd的使用; 多样品整合[MergeSeurat, RunMultiCCA, AlignSubspace] Trajectory 2个命运; 宽变长 melt; 
	细胞比例图:
	```
	freq_table <- prop.table(x = table(immune.combined2@ident, immune.combined2@meta.data[, "Type"]), 
	margin = 2)
	freq_table <- as.data.frame(freq_table)
	ggplot(freq_table, aes(x=Var1, y=Freq, fill=Var2)) + 
	geom_bar(stat="identity", position="fill", color="black", lwd=0.25) + 
	theme(axis.title.x = element_blank())
	ggsave("CompositionProp.pdf", width=4, height=2)
	```




<h2>Code: GitHub</h2>

b01-PMID-38200243
	2024 Nat Cancer
	https://www.nature.com/articles/s43018-023-00691-z
	https://github.com/snow55/SIRPAproject/blob/main/Scripts/3_find_DEGs_and_plot.r
	优点: 文件编号，带中文注释


b02-PMID-32649887
	2020 Cancer Cell: 血液肿瘤的免疫景观
	优点: 血液相关


b03-PMID-39047727
	2024 Cell: pan-cancer B
	code: https://github.com/yuyang3/pan-B
	fig: http://pan-b.cancer-pku.cn/
	优点: web可视化包 Cirro; Cancer_color_panel 颜色组合; 
		对亚群做 scVelo https://github.com/yuyang3/pan-B/blob/main/Figure6_trajectory_analysis.ipynb

b04-PMID-39315707
	2024 NAR: scMMO-atlas
	https://zenodo.org/records/13710871
	优点: shiny 界面值得学习，但是没提供代码

b05-PMID-:
	Towards modeling context-specific EMT regulatory networks using temporal single cell RNA-Seq data
	https://lusystemsbio.github.io/EMT-Cancer-scRnaSeq/EMT-Cancer-scRnaSeq.html  新版本; 老版本 _v1.html
	https://github.com/lusystemsbio/EMT-Cancer-scRnaSeq

b06-PMID-37646019:
	2023 iScience; CD4T Act vs Rest
	https://github.com/JinWLab/T_active
	优点: 配色可以参考;

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